A Rough Set-based Approach to Handling Uncertainty in Geographic Data Classification

2007 ◽  
pp. 75-87
Author(s):  
Piotr Jankowski
Keyword(s):  
Rough Set ◽  
Data Classification ◽  
Geographic Data

Data classification based on tolerant rough set

2001 ◽  
Vol 34 (8) ◽  
pp. 1613-1624 ◽  
Author(s):  
Daijin Kim
Keyword(s):  
Rough Set ◽  
Data Classification

scholarly journals Rough set based ensemble learning algorithm for agricultural data classification

Filomat ◽  
2018 ◽  
Vol 32 (5) ◽  
pp. 1917-1930 ◽  
Author(s):  
Lei Shi ◽  
Qiguo Duan ◽  
Juanjuan Zhang ◽  
Lei Xi ◽  
Hongbo Qiao ◽  
...  
Keyword(s):  
Ensemble Learning ◽  
Rough Set ◽  
Rough Set Theory ◽  
Learning Algorithm ◽  
Uncertain Data ◽  
Data Classification ◽  
Research Area ◽  
Experimental Comparison ◽  
Classification Problems ◽  
Ensemble Learning Algorithm

Agricultural data classification attracts more and more attention in the research area of intelligent agriculture. As a kind of important machine learning methods, ensemble learning uses multiple base classifiers to deal with classification problems. The rough set theory is a powerful mathematical approach to process unclear and uncertain data. In this paper, a rough set based ensemble learning algorithm is proposed to classify the agricultural data effectively and efficiently. An experimental comparison of different algorithms is conducted on four agricultural datasets. The results of experiment indicate that the proposed algorithm improves performance obviously.

KNNHI: Resilient KNN algorithm for heterogeneous incomplete data classification and K identification using rough set theory

2022 ◽  
pp. 016555152110695
Author(s):  
Ahmed Hamed ◽  
Mohamed Tahoun ◽  
Hamed Nassar
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Structural Damage ◽  
Missing Values ◽  
Rough Set Theory ◽  
Data Classification ◽  
Recall Accuracy ◽  
K Value ◽  
Almost All ◽  
The University

The original K-nearest neighbour ( KNN) algorithm was meant to classify homogeneous complete data, that is, data with only numerical features whose values exist completely. Thus, it faces problems when used with heterogeneous incomplete (HI) data, which has also categorical features and is plagued with missing values. Many solutions have been proposed over the years but most have pitfalls. For example, some solve heterogeneity by converting categorical features into numerical ones, inflicting structural damage. Others solve incompleteness by imputation or elimination, causing semantic disturbance. Almost all use the same K for all query objects, leading to misclassification. In the present work, we introduce KNNHI, a KNN-based algorithm for HI data classification that avoids all these pitfalls. Leveraging rough set theory, KNNHI preserves both categorical and numerical features, leaves missing values untouched and uses a different K for each query. The end result is an accurate classifier, as demonstrated by extensive experimentation on nine datasets mostly from the University of California Irvine repository, using a 10-fold cross-validation technique. We show that KNNHI outperforms six recently published KNN-based algorithms, in terms of precision, recall, accuracy and F-Score. In addition to its function as a mighty classifier, KNNHI can also serve as a K calculator, helping KNN-based algorithms that use a single K value for all queries that find the best such value. Sure enough, we show how four such algorithms improve their performance using the K obtained by KNNHI. Finally, KNNHI exhibits impressive resilience to the degree of incompleteness, degree of heterogeneity and the metric used to measure distance.

Maximum relevance, minimum redundancy band selection based on neighborhood rough set for hyperspectral data classification

2016 ◽  
Vol 27 (12) ◽  
pp. 125501 ◽  
Author(s):  
Yao Liu ◽  
Yuehua Chen ◽  
Kezhu Tan ◽  
Hong Xie ◽  
Liguo Wang ◽  
...  
Keyword(s):  
Rough Set ◽  
Data Classification ◽  
Hyperspectral Data ◽  
Band Selection ◽  
Neighborhood Rough Set

The Application of Rough Set Technique for Missing Data

2010 ◽  
Vol 439-440 ◽  
pp. 1052-1056
Author(s):  
Bo Li ◽  
Er Liang Zhang
Keyword(s):  
Missing Data ◽  
Rough Set ◽  
Data Classification ◽  
Classification Algorithms

In this paper, a missing data classification algorithms based on rough technique is proposed, and the complexity of the algorithms is analyzed, finally a missing data classification experiment with a typical dataset is conducted. The result of experimentation shows the algorithms not only can effectively improve the accuracy and efficiency of classification while enormously reducing the number of attributes, but also have the good performance on noises control.

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